I followed the tutorial of object detection in mxnet,
http://mxnet.io/tutorials/computer_vision/detection.html
but I don't download the pretrained network and place the extracted file final-0000.params.
Where place I can find the file to download in the Internet?
You can find instructions for downloading the model params for a variety of pre-trained models here: https://github.com/dmlc/mxnet-model-gallery
and the model files themselves are hosted here: http://data.dmlc.ml/models/
You can pick from a variety of pre-trained models that have varying levels of complexity (computational cost) and accuracy, as well as picking between having 1,000 classes in the standard ImageNet ILSVRC-2012 task, or the full 21,000 class dataset which is more expressive. Note that the exact filename of the params file varies.
You can also view various pre-trained MXNet models in MXNet model zoo page. http://mxnet.io/model_zoo/index.html
Related
Does a tflite file contain data about the model architecture? A graph that shows what operations there where between the weights and features and biases, what kind of layers (linear or convolutional etc), size of layers, and what activation functions are there in-between the layers?
For example a graph you get with graphviz, that contains all the information, or does a tflite file only contain the final weights of the model after training?
I am working on a project with image style transfer. I wanted to do some research on an existing project, and see what parameters work better. The project I am looking at is here:
https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3-dynamic-shapes/int8/transfer/1
I can download a tflite file, but I don't know much about these files. If they have the architecture I need, how do I read it?
TFLite flatbuffer files contain the model structure as well. For example, there are a subgraph concept in TFLite, which corresponds to the function concept in the programming language and the operator nodes also represent a graph node, which takes inputs and generates outputs. By using the Netron application, the model architecture can be visualized.
I want train a custom dataset on FasterRCNN with Mobilenetv1 or v2. I want to use the pre-trained models in tensorflow zoo. But I cant find faster Rcnn model with mobilenet as base extractor. Where can I get it?
I have already tensorflow zoo in github. I have previous used SSD+Mobilenet config for the same. Now I want to compare the results with FasterRCNN and RCNN with Mobilenet.
The official repo has not released Faster RCNN with mobilenet models yet. But if you want you can still use some other models with mobilenet trained on COCO, the process is a bit complicated.
There are two important steps to proceed.
First one is to have corresponding feature extractor class. For Faster RCNN, the models directory already contains faster_rcnn_mobilenet feature extractor implementation so this step is OK. But for R-FCN, you will have to implement the feature extractor class yourself.
Second one is to change tensor names available in the checkpoint. For example, if you use ssd_mobilenet_v1_xxx as checkpoint, then all tensors within mobilenet scope are named as FeatureExtractor/MobilenetV1/XXX while if in the faster_rcnn_mobilenet_v1 model, the tensor names within mobilenet scope are FirstStageFeatureExtractor/MobilenetV1/XXX (and SecondStageFeatureExtractor/MobilenetV1/XXX). So essentially you need to remove FirstStage (as well as SecondStage) in the names of all feature extractor tensors, then these tensors will have exactly the same name as in the checkpoint, and will be correctly restored. If you do this, the function you need to modify is
def restore_map(self,
fine_tune_checkpoint_type='detection',
load_all_detection_checkpoint_vars=False):
in file faster_rcnn_meta_arch.py.
I am starting to work with Intel movidius neural compute stick.
To start working, in my case, it is necessary to download pretrained models.
In the tutorials they refer to http://download.tensorflow.org/models/.
However, there is not a list that shows all the models available for download.
If the latest version of a net, lets say inception_v4, is not known, I cannot download the corresponding .tar.gz file.
Does anyone know a method to have an updated list of the .tar.gz files of the pretrained models available for download?
Thanks
The following two links may help
detection_model_zoo
TensorFlow-Slim image classification model library
keras.applications makes it easy to load models and their pretrained weights if you can use Keras
Considering that most of the posted linked outdated, I suggest looking into the TensorFlow hub (https://www.tensorflow.org/hub) for more recent pre-trained models.
I am building a new tensorflow model based off of SSD V1 coco model in order to perform real time object detection in a video but i m trying to find if there is a way to build a model where I can add a new class to the existing model so that my model has all those 90 classes available in SSD MOBILENET COCO v1 model and also contains the new classes that i want to classify.
For example, I have created training data for two classes: man, woman
Now, I built a new tensorflow model that identifies a man and/or woman in a video. However, my model does not have the other 90 classes present in original SSD Mobilenet model. I am looking for a way to concatenate both models or pass more than one model to my code to detect the objects.
If you have any questions or if I am not clear, please feel free to probe me further.
The only way i find is you need to get dataset of SSD Mobilenet model on which it was trained.
Make sure all the images are present in one directory and annotations in another directory.
We should have a corresponding annotation file for each image file
ex: myimage.jpg and myimage.xml
If all the images of your customed dataset are of same formate with SSD Mobilenet model then annotate it with a tool called LabelImg.
Add that images and annotated files to respective images and annotations directory where we have already saved SSD Mobilenet.
Try regenerate new TFrecord and continue with remaining procedure on it.
You can use transfer learning with Tensorflow API.
Transfer learning allows you to load re-trained network and modify the fully connected layer by introducing your classes.
There is full description for this in the following references:
Codelab
A good explanation here
Tensorflow API here for more details
Also you can use google cloud platform for better and faster results:
I wish this helps you.
I don't think there is a way you can add your classes to the existing 90 classes without using the dataset it is previously trained with. Your only way is to use that dataset plus your own and retrain the model.
I could download and successfully test brain parcellation demo of NiftyNet package. However, this only gives me the ultimate parcellation result of a pre-trained network, whereas I need to get access to the output of the intermediate layers too.
According to this demo, the following line downloads a pre-trained model and a test MR volume:
wget -c https://www.dropbox.com/s/rxhluo9sub7ewlp/parcellation_demo.tar.gz -P ${demopath}
where ${demopath} is the path to the demo folder. Extracting the downloaded file will create a .ckpt file which seems to contain a pre-trained tensorflow model, however I could not manage to load it into a tensorflow session.
Is there a way that I can load the pre-trained model and have access to the all its intermediate activation maps? In other words, how can I load the pre-trained models from NiftyNet library into a tensorflow session such that I can explore through the model or probe certain intermediate layer for a any given input image?
Finally, in NiftyNet's website it is mentioned that "a number of models from the literature have been (re)implemented in the NiftyNet framework". Are pre-trained weights of these models also available? The demo is using a pre-trained model called HighRes3DNet. If the pre-trained weights of other models are also available, what is the link to download those weights or saved tensorflow models?
To answer your 'Finally' question first, NiftyNet has some network architectures implemented (e.g., VNet, UNet, DeepMedic, HighRes3DNet) that you can train on your own data. For a few of these, there are pre-trained weights for certain applications (e.g. brain parcellation with HighRes3DNet and abdominal CT segmentation with DenseVNet).
Some of these pre-trained weights are linked from the demos, like the parcellation one you linked to. We are starting to collect the pre-trained models into a model zoo, but this is still a work in progress.
Eli Gibson [NiftyNet developer]